As demand for data and data science expands rapidly, significant research into potential uses of data in various applications are also increasing at a dramatic rate. With enormous amounts of data and information becoming increasingly available, utilizing data is becoming a greater focus of both consumer and commercial activities alike. Datasets (i.e., sets or groups of logically-related data and/or information) are being created to provide statistical information that researchers are using to discover new innovations and applications in almost every aspect of contemporary life and lifestyles. However, utilizing data also involves addressing a growing problem, which includes identifying data, sources thereof, and managing the ever-increasing amount of data becoming available. Moreover, as the amount and complexity of data, datasets, databases, datastores and data storage facilities increase, the ability to identify, locate, retrieve, analyze, and present data in useful ways is also becoming increasingly difficult. Today, managing large amounts of data for useful purposes poses a significant problem for individual users, organizations, and entities alike. Conventional techniques are problematic in that these are neither capable nor configured to manage large scale problems such as providing integrated access to data that is both available on public resources as well as those that are hosted or stored on private (i.e., secure (i.e., requiring authentication or authorization before access is permitted)) data storage resources. More importantly, users are typically burdened by conventional techniques in that access to data often requires not only proficient, if not expert, knowledge of both computer programming languages commonly known and used by data researchers and scientists (e.g., Python, or others), but knowledge of complex computer databases, datastores, data repositories, data warehouses, data and object schema, data modeling, graph modeling, graph data, linked data, and numerous other data science topics is also required. Queries executed to retrieve data using conventional techniques typically require knowledge of specific programming or formatting languages, which can limit the usability of data. Specifically, conventional techniques are problematic because these lack intrinsic knowledge or technical functionality to permit a user such as a data scientist to locate, manage, access, and execute queries to retrieve data from various disparate and often dissimilar data resources.
Thus, what is needed is a solution for managing consolidated, integrated access to public and/or privately-accessible (i.e., secure) data without the limitations of conventional techniques.